Network security situation prediction based on singular spectrum analysis

Network security situation prediction based on historical data is to provide warning in advance with network administrator. However, the current proposed approaches ignores the fact that the original time series of security situation include the random component which is not able to be forecast. Singular spectrum analysis is utilized to separate the random component. After that, ARMA model is used to predicate the security situation value in near future. The experiment shows the prediction benefits from the separation.